Instructions to use nullptr04/CoEvo-RSI-who-when with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nullptr04/CoEvo-RSI-who-when with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="nullptr04/CoEvo-RSI-who-when") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("nullptr04/CoEvo-RSI-who-when") model = AutoModelForMultimodalLM.from_pretrained("nullptr04/CoEvo-RSI-who-when", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nullptr04/CoEvo-RSI-who-when with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nullptr04/CoEvo-RSI-who-when" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nullptr04/CoEvo-RSI-who-when", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/nullptr04/CoEvo-RSI-who-when
- SGLang
How to use nullptr04/CoEvo-RSI-who-when with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "nullptr04/CoEvo-RSI-who-when" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nullptr04/CoEvo-RSI-who-when", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "nullptr04/CoEvo-RSI-who-when" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nullptr04/CoEvo-RSI-who-when", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use nullptr04/CoEvo-RSI-who-when with Docker Model Runner:
docker model run hf.co/nullptr04/CoEvo-RSI-who-when
CoEvoX · who_when model-harness pair
Model-harness pair for the who_when domain from CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?
| Domain | who_when |
| Model | repo root (Qwen3.5-9B + full-parameter SFT) |
| Harness | harness/ |
| Paper | arXiv:2610.25078 |
Reported scores
Failure attribution (agent / step accuracy, %), evaluated with the harness/ in this repo. The test benchmarks are held out from both training and evolutionary selection.
| Benchmark | Who (agent) | When (step) |
|---|---|---|
| Who&When Hand-Crafted | 68.39 | 29.31 |
| Who&When Algorithm-Generated | 62.17 | 44.71 |
| Who&When Pro | 69.43 | 57.39 |
| TraceElephant | 70.78 | 28.92 |
Layout
. # model weights (config.json, *.safetensors, tokenizer, ...)
harness/ # harness source for this pair
Inference
Serve the model with vLLM. The tool-call and reasoning parsers are required, because the harness talks to the model through tool calls.
huggingface-cli download nullptr04/CoEvo-RSI-who-when --local-dir ckpts/CoEvo-RSI-who-when
CUDA_VISIBLE_DEVICES=0 vllm serve ckpts/CoEvo-RSI-who-when \
--host 127.0.0.1 --port 8000 \
--served-model-name CoEvo-RSI-who-when \
--max-model-len 65536 \
--enable-auto-tool-choice --tool-call-parser qwen3_coder \
--reasoning-parser qwen3
Then, from the CoEvoX repository, run the evaluation with the evolved harness (generated, the same as harness/ here). The script reads the model from POLICY_BASE_URL (default http://127.0.0.1:8000/v1).
bash scripts/run_whowhen_full_test.sh generated
Citation
If you use this model or harness, please cite:
@misc{su2026coevorsiharnessesevolvemodels,
title={CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?},
author={Jiaqi Su and Guibin Zhang and Jie-Jing Shao and Cong Pang and Tiankuo Yao and Ivor Tsang and Haiyan Yin and Shuicheng Yan},
year={2026},
eprint={2610.25078},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2610.25078},
}
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